气候变化领域集成服务门户(CCPortal): Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications

CCPortal

DOI	10.1016/j.rse.2020.112136
	Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications
	Wei J.; Li Z.; Lyapustin A.; Sun L.; Peng Y.; Xue W.; Su T.; Cribb M.
发表日期	2021
ISSN	00344257
卷号	252
英文摘要	Exposure to fine particulate matter (PM2.5) can significantly harm human health and increase the risk of death. Satellite remote sensing allows for generating spatially continuous PM2.5 data, but current datasets have overall low accuracies with coarse spatial resolutions limited by data sources and models. Air pollution levels in China have experienced dramatic changes over the past couple of decades. However, country-wide ground-based PM2.5 records only date back to 2013. To reveal the spatiotemporal variations of PM2.5, long-term and high-spatial-resolution aerosol optical depths, generated by the Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle implementation of Atmospheric Correction (MAIAC) algorithm, were employed to estimate PM2.5 concentrations at a 1 km resolution using our proposed Space-Time Extra-Trees (STET) model. Our model can capture well variations in PM2.5 concentrations at different spatiotemporal scales, with higher accuracies (i.e., cross-validation coefficient of determination, CV-R2 = 0.86–0.90) and stronger predictive powers (i.e., R2 = 0.80–0.82) than previously reported. The resulting PM2.5 dataset for China (i.e., ChinaHighPM2.5) provides the longest record (i.e., 2000 to 2018) at a high spatial resolution of 1 km, enabling the study of PM2.5 variation patterns at different scales. In most places, PM2.5 concentrations showed increasing trends around 2007 and remained high until 2013, after which they declined substantially, thanks to a series of government actions combating air pollution in China. While nationwide PM2.5 concentrations have decreased by 0.89 μg/m3/yr (p < 0.001) during the last two decades, the reduction has accelerated to 4.08 μg/m3/yr (p < 0.001) over the last six years, indicating a significant improvement in air quality. Large improvements occurred in the Pearl and Yangtze River Deltas, while the most polluted region remained the North China Plain, especially in winter. The ChinaHighPM2.5 dataset will enable more insightful analyses regarding the causes and attribution of pollution over medium- or small-scale areas. © 2020 Elsevier Inc.
英文关键词	1 km resolution; ChinaHighPM2.5; MODIS; PM2.5; Space-Time Extra-Trees model
语种	英语
scopus关键词	Air quality; Health risks; Image resolution; Public policy; Radiometers; Remote sensing; River pollution; Air pollution in chinas; Coefficient of determination; Fine particulate matter (PM2.5); High spatial resolution; Moderate resolution imaging spectroradiometer; Multi-angle implementation of atmospheric corrections; Satellite remote sensing; Spatio-temporal variation; Trees (mathematics); aerosol composition; air quality; algorithm; atmospheric pollution; data quality; MODIS; particle size; particulate matter; spatial resolution; China; North China Plain; Yangtze River
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179067
作者单位	State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China; Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, United States; Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, MD, United States; College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
推荐引用方式 GB/T 7714	Wei J.,Li Z.,Lyapustin A.,et al. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications[J],2021,252.
APA	Wei J..,Li Z..,Lyapustin A..,Sun L..,Peng Y..,...&Cribb M..(2021).Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications.Remote Sensing of Environment,252.
MLA	Wei J.,et al."Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications".Remote Sensing of Environment 252(2021).